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kindrat86

mcp-deal-flow-signal

get_trending_startups

Identify startups with strong GitHub momentum by analyzing engineering acceleration signals, including commit velocity changes and contributor growth across 20 sectors.

Instructions

Get the top 20 trending startups by engineering acceleration across all sectors. Returns startup name, commit velocity change, contributors, signal type, and sector. Use this to find which startups are showing the strongest GitHub momentum right now.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns exactly 20 results ('top 20'), includes specific data fields in the response, and focuses on current momentum ('right now'). However, it doesn't mention potential limitations like rate limits, authentication needs, or data freshness guarantees.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two well-structured sentences with zero waste. The first sentence defines purpose and output, the second provides usage guidance. Every word earns its place, and the most important information (what it does) comes first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter read-only tool with no output schema, the description is reasonably complete. It specifies the exact number of results, the ranking criteria, the data fields returned, and the use case. The main gap is lack of information about response format/structure, which would be important for an agent to process the output correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the baseline would be 3. The description adds value by clarifying that no filtering parameters are needed - it automatically returns 'across all sectors' and 'top 20' results. This semantic context about the tool's automatic scoping elevates the score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get the top 20 trending startups'), resource ('startups'), and scope ('by engineering acceleration across all sectors'). It distinguishes from siblings by focusing on trending startups rather than methodology, summaries, individual signals, or sector-based searches.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use it ('to find which startups are showing the strongest GitHub momentum right now'), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools. The implied alternative would be using other tools for different purposes, but this isn't explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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